Models

Ease of Use

Priorities

Performance

Expert productivity

Compute utilization

Wall-clock efficiency

Variety of use cases

Competing objectives

Constraints

Conditional parameters

Long training cycles

Proven Results

By optimizing each model, teams who invest in automated hyperparameter optimization will amplify the performance and impact of models on their business. SigOpt’s customers also typically realize higher performance from models tuned with our solution, and, in certain cases, use SigOpt to solve challenges that unlock entirely new business opportunity.

Accelerate Wall-Clock Time to Tune

SigOpt’s proprietary solution efficiently explores and exploits your search space to uncover the global optima for any model much faster than grid search, random search or open source Bayesian optimization.

Increase Computational Efficiency

Tune models with a computationally efficient optimization solution and maximize the utilization of your clusters. This process is critical to scaling machine learning in production.

Improve Model Performance

While there is no free lunch, SigOpt consistently outperforms other optimizers across any variety of models that your team develops to solve different business challenges.

Learn More About Our Optimization Approach

Optimization is in our DNA. SigOpt was founded by the creator of the Metric Optimization Engine (MOE) and our research team has spent years building and refining our ensemble of applied optimization algorithms to scale with our enterprise customers’ needs. Built by experts for experts, we have a deep appreciation for the broader research community from which we draw inspiration and to which we regularly contribute.